ANLY 500 will focus on the foundations of:

• Data Analytics,
• Research Methodology, and
• R programming Language.

The first question we must ask ourselves in this course is: What is Analytics?

We should note analytics can be defined in two ways!

The utilization of:

• data,
• information technologies (R software, Python, SAS, etc.)
• statistical analysis
• quantitative and qualitative methods,
• mathematical or computer based models to help decision making through improved insights.

The focus of data analytics can be defined under three scopes, including:

• (1) Descriptive Analytics
• (2) Predictive Analytics
• (3) Prescriptive Analytics

Dataset: “Sunspot Trends from 1749-01-01 to 2013-09-01’”

Description: Understand the Historical Trend of Sunspots from 1749 to 2013.

• Notice, we’re accessing data.
• Here we use the datasets package available in R:

Analytics is the discovery, interpretation and communication of meaningful patterns or summary of data using data analytics.

• Note that Analytics is a mindset and Data Analytics is a process.
• So conducting analytical research is based on a way of thinking and acting on data.

Now we should be asking the question: What is Data Analytics?

High level analysis techniques commonly used in data analytics include:

• Exploratory Data Analysis (EDA): which aims to find patterns and relationships in data.
• Confirmatory Data Analysis (CDA): which applies statistical techniques to determine whether hypotheses about a data set are true or false.

However, two other types of analysis may be considered.

Quantitative data analysis: involves analysis of numerical data with quantifiable variables that can be compared or measured statistically.

• Testing theories using numbers.

Qualitative data analysis: it is more interpretive. It focuses on understanding the content of non-numerical data like text, images, audio and video, including common phrases, themes and points of view.

• Testing theories using language.

In other words, formulate a question that needs to be answered.

• Observe the real world
• Read other research
• Ask others about the problem

Test the concept:

• Collect data to see whether your hunch is correct.
• To do this you need to define variables.
• Variables are anything that can be measured and can differ across entities or time.

Theory:

• Definition - A hypothesized general principle or set of principles that explains known findings about a topic and from which new hypotheses can be generated.

Hypothesis:

• Definition - A prediction from a theory.
• Should be clear and understandable
• Should be testable
• Should be measurable

Falsification:

• Definition - The act of disproving a theory or hypothesis.

Independent Variable:

• The proposed cause
• A predictor variable
• A manipulated variable (in experiments)

Dependent Variable:

• The proposed effect
• An outcome variable
• Measured not manipulated (in experiments)

Data is a set of values/measurements of quantitative or qualitative variables.

• It is information in a raw or unstructured form.
• It can consist of fact, figure, characters, symbols, etc.

In a dataset, we can distinguish two types of variables:

• (1) Categorical and,
• (2) Continuous.

Definition - entities that are divided into distinct categories.

Includes the following:

• Binary variables
• Nominal variables
• Ordinal variables

R stores categorical variables as a factor or character.

Factors are the variables in R which take on a limited number of different values.

Definition - a binary variable is only two categories.

• Dead or alive
• On or off

Definition - A nominal variable is more than two categories.

• Whether someone is an Assistant, Associate, or Full Professor.

Definition - A ordinal variable is the same as a nominal, but the categories have a logical order.

• Whether people got a fail, a pass, a merit or a distinction in their exam.
• If you classify economic status, with three categories (low, medium and high).

In addition to being able to classify values into categories, you can order the categories: first, second, third

Definition - entities get a distinct score.

Includes the following:

• Interval variables
• Ratio variables

Definition - A interval variable is equal intervals on the variable. It represents equal differences in the property being measured. This variable also does not have a true zero.

• i.e. The difference between 6 and 8 is equivalent to the difference between 13 and 15.
• i.e. Temperature of zero does not represent an lack of temperature

Definition - A ratio variable is the same as an interval variable, but the ratios of scores on the scale must also make sense. This variable does have a true zero.

• i.e. A score of 16 on an anxiety scale means that the person is, in reality, twice as anxious as someone scoring 8.

The accuracy of the measurements are key to your solutions.

Measurement Error: - aka observational error

• Definition - The discrepancy between the actual value we’re trying to measure, and the number we use to represent that value.

  • i.e. You (in reality) weigh 80 kg.
  • i.e. You stand on your bathroom scales and they say 83 kg.
  • i.e. The measurement error is 3 kg.

Validity:

• Definition - Whether an instrument measures what it set out to measure.

Including the following:

• Content validity - Evidence that the content of a test corresponds to the content of the construct it was designed to cover.
• Ecological validity - Evidence that the study, experiment or test can be applied, and allow inferences, to real-world conditions.

Reliability:

• Definition - The ability of the measure to produce the same results under the same conditions.

Test-Retest Reliability:

• Definition - The ability of a measure to produce consistent results when the same entities are tested at two different points in time.

To use measures in any research and test them we must now understand the following: How to Measure?

It is different for certain types of research, including:

• Cross-sectional research
• Longitudinal research
• Correlational research
• Experimental research

Definition - One or more variables is systematically manipulated to see their effect (alone or in combination) on an outcome variable.

• Statements can be made about cause and effect.

Cause and Effect (Hume, 1748)

• 1. Cause and effect must occur close together in time (contiguity).
• 2. The cause must occur before an effect does.
• 3. The effect should never occur without the presence of the cause.

Confounding variables: the ‘Tertium Quid’

• A variable (that we may or may not have measured) other than the predictor variables that potentially affects an outcome variable.
• The relationship between ice cream sales and accidental drowning is confounded by the weather.

Ruling out confounds (Mill, 1865)

• An effect should be present when the cause is present and that when the cause is absent the effect should be absent also.
• Control conditions: the cause is absent.

Considering the what & how to measure, we must now look at the methods of data collection.

For instance:

• Between-group/between-subject/independent

  • Different entities in experimental conditions

• Repeated-measures (within-subject)

  • The same entities take part in all experimental conditions.
  • Economical
  • Practice effects
  • Fatigue

Systematic Variation

• Definition - Differences in performance created by a specific experimental manipulation.

Unsystematic Variation

• Definition - Differences in performance created by unknown factors

Randomization

• Definition - random selection or assignment equalizes unsystematic variation in the study.

First, populations and samples should be understood so that your analysis is not misleading when interpreting results.

Population

• Definition - the collection of units (be they people, plankton, plants, cities, suicidal authors, etc.) to which we want to generalize a set of findings or a statistical model.

Sample

• Definition - A smaller (but hopefully representative) collection of units from a population used to determine truths about that population.

A simple statistical model can be used to analyze data.

• We use a statistical model to represent what is happening in the real world.

For instance, the mean is a hypothetical value.

• It doesn’t have to be a value that actually exists in the data set.
• As such, the mean is simple statistical model.

• The numbers estimated from a single test/study/experiment are considered a sample.

• Parameters = Greek Symbols

• Statistics = Latin Letters

To analyze the data and generate interpretable results the following statistical models can be used:

• Frequency distribution, measures of shape
• Central tendencies, measures of distance
• Dispersion, measures of spread
• Going beyond the data with z-scores and association.

In this lecture, you have learned:

• An introduction to data analytics
• Theories and hypotheses
• Measurement of variables
• Types of variables
• Types of research design
• Issues and considerations for data analysis